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Article: Regression estimator in ranked set sampling

TitleRegression estimator in ranked set sampling
Authors
KeywordsAuxiliary information
Concomitant variable
Double sampling
Ranked set sampling
Regression estimator
Relative precision
Simple random sampling
Issue Date1997
PublisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/BIOM
Citation
Biometrics, 1997, v. 53 n. 3, p. 1070-1080 How to Cite?
AbstractRanked set sampling (RSS) utilizes inexpensive auxiliary information about the ranking of the units in a sample to provide a more precise estimator of the population mean of the variable of interest Y, which is either difficult or expensive to measure. However, the ranking may not be perfect in most situations. In this paper, we assume that the ranking is done on the basis of a concomitant variable X. Regression-type RSS estimators of the population mean of Y will be proposed by utilizing this concomitant variable X in both the ranking process of the units and the estimation process when the population mean of X is known. When X has unknown mean, double sampling will be used to obtain an estimate for the population mean of X. It is found that when X and Y jointly follow a bivariate normal distribution, our proposed RSS regression estimator is more efficient than RSS and simple random sampling (SRS) naive estimators unless the correlation between X and Y is low (|p| < 0.4). Moreover, it is always superior to the regression estimator under SRS for all ρ. When normality does not hold, this approach could still perform reasonably well as long as the shape of the distribution of the concomitant variable X is only slightly departed from symmetry. For heavily skewed distributions, a remedial measure will be suggested. An example of estimating the mean plutonium concentration in surface soil on the Nevada Test Site, Nevada, U.S.A., will be considered.
Persistent Identifierhttp://hdl.handle.net/10722/82902
ISSN
2015 Impact Factor: 1.36
2015 SCImago Journal Rankings: 1.906
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYu, PLHen_HK
dc.contributor.authorLam, Ken_HK
dc.date.accessioned2010-09-06T08:34:42Z-
dc.date.available2010-09-06T08:34:42Z-
dc.date.issued1997en_HK
dc.identifier.citationBiometrics, 1997, v. 53 n. 3, p. 1070-1080en_HK
dc.identifier.issn0006-341Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/82902-
dc.description.abstractRanked set sampling (RSS) utilizes inexpensive auxiliary information about the ranking of the units in a sample to provide a more precise estimator of the population mean of the variable of interest Y, which is either difficult or expensive to measure. However, the ranking may not be perfect in most situations. In this paper, we assume that the ranking is done on the basis of a concomitant variable X. Regression-type RSS estimators of the population mean of Y will be proposed by utilizing this concomitant variable X in both the ranking process of the units and the estimation process when the population mean of X is known. When X has unknown mean, double sampling will be used to obtain an estimate for the population mean of X. It is found that when X and Y jointly follow a bivariate normal distribution, our proposed RSS regression estimator is more efficient than RSS and simple random sampling (SRS) naive estimators unless the correlation between X and Y is low (|p| < 0.4). Moreover, it is always superior to the regression estimator under SRS for all ρ. When normality does not hold, this approach could still perform reasonably well as long as the shape of the distribution of the concomitant variable X is only slightly departed from symmetry. For heavily skewed distributions, a remedial measure will be suggested. An example of estimating the mean plutonium concentration in surface soil on the Nevada Test Site, Nevada, U.S.A., will be considered.en_HK
dc.languageengen_HK
dc.publisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/BIOMen_HK
dc.relation.ispartofBiometricsen_HK
dc.rightsBiometrics. Copyright © Blackwell Publishing Ltd.en_HK
dc.subjectAuxiliary informationen_HK
dc.subjectConcomitant variableen_HK
dc.subjectDouble samplingen_HK
dc.subjectRanked set samplingen_HK
dc.subjectRegression estimatoren_HK
dc.subjectRelative precisionen_HK
dc.subjectSimple random samplingen_HK
dc.titleRegression estimator in ranked set samplingen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0006-341X&volume=53&spage=1070&epage=1080&date=1997&atitle=Regression+estimator+in+ranked+set+samplingen_HK
dc.identifier.emailYu, PLH: plhyu@hkucc.hku.hken_HK
dc.identifier.authorityYu, PLH=rp00835en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.2307/2533564en_HK
dc.identifier.pmid9333340-
dc.identifier.scopuseid_2-s2.0-0030818603en_HK
dc.identifier.hkuros31889en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0030818603&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume53en_HK
dc.identifier.issue3en_HK
dc.identifier.spage1070en_HK
dc.identifier.epage1080en_HK
dc.identifier.isiWOS:A1997XV52300023-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridYu, PLH=7403599794en_HK
dc.identifier.scopusauthoridLam, K=36492945700en_HK

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